Year: 2024
Type: article
Abstract: This article highlights a collection of ideas with an underlying deceptive simplicity that addresses several practical challenges in computational social science and generative AI safety. These ideas lead to (1) an interpretable and quantifiable framework for political polarization; (2) a language identifier robust to noisy social media text settings; (3) a cross‐lingual semantic sampler that harnesses code‐switching; and (4) a bias audit framework that uncovers shocking racism, antisemitism, misogyny, and other biases in a wide suite of large language models.
Source: AI Magazine
DOI: 10.1002/aaai.12204
Author: Ashiqur R. KhudaBukhsh
Institution: Rochester Institute of Technology
Language: English
Subfield: Safety Research
Field: Social Sciences
Domain: Social Sciences
Open Access status: hybrid
Estimated APC paid: $2,400
OpenAlex ID: W4403657563